Estimating propensity scores with missing covariate data using general location mixture models
In many observational studies, analysts estimate causal effects using propensity scores, e.g. by matching, sub-classifying, or inverse probability weighting based on the scores. Estimation of propensity scores is complicated when some values of the covariates are missing. Analysts can use multiple i...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
2011-03.
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| Subjects: | |
| Online Access: | Get fulltext |